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The blockage of water intakes by ice is recurrent in northern rivers during winter. Previous field studies have monitored field conditions leading to ice blockage and provided a review of mitigations methods. However, to improve the efficacy of these measures, the mechanisms that create the blockage need to be locally measured. For this purpose, a field campaign was implemented to monitor a water intake on the Mille-Iles River at Terrebonne, Quebec, during the winter of 2020–2021. Results from this study showed that ice accumulation on the trash rack had an average growth rate of 1.35 cm/h and reached a maximum thickness of 24 cm. The release rate of these trash rack accumulation events was on average 1.8 cm/h, which is 30% faster than the deposition rate. A minimum cumulative degree minutes of supercooling of 4.5 °C.min was required for the start of a trash-rack ice-accumulation event.

Abstract. Efficient adaptation strategies to climate change require the estimation of future impacts and the uncertainty surrounding this estimation. Over- or underestimating future uncertainty may lead to maladaptation. Hydrological impact studies typically use a top-down approach in which multiple climate models are used to assess the uncertainty related to the climate model structure and climate sensitivity. Despite ongoing debate, impact modelers have typically embraced the concept of “model democracy”, in which each climate model is considered equally fit. The newer Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations, with several models showing a climate sensitivity larger than that of Phase 5 (CMIP5) and larger than the likely range based on past climate information and understanding of planetary physics, have reignited the model democracy debate. Some have suggested that “hot” models be removed from impact studies to avoid skewing impact results toward unlikely futures. Indeed, the inclusion of these models in impact studies carries a significant risk of overestimating the impact of climate change. This large-sample study looks at the impact of removing hot models on the projections of future streamflow over 3107 North American catchments. More precisely, the variability in future projections of mean, high, and low flows is evaluated using an ensemble of 19 CMIP6 general circulation models (GCMs), 5 of which are deemed hot based on their global equilibrium climate sensitivity (ECS). The results show that the reduced ensemble of 14 climate models provides streamflow projections with reduced future variability for Canada, Alaska, the Southeast US, and along the Pacific coast. Elsewhere, the reduced ensemble has either no impact or results in increased variability in future streamflow, indicating that global outlier climate models do not necessarily provide regional outlier projections of future impacts. These results emphasize the delicate nature of climate model selection, especially based on global fitness metrics that may not be appropriate for local and regional assessments.

The Hudson Bay basin is a large contributor of freshwater input in the Arctic Ocean and is also an area affected by destructive spring floods. In this study, the hydrological model MESH (Modelisation Environmentale Communautaire - Surface and hydrology) was set up for the Groundhog River watershed situated in the Hudson Bay basin, to simulate the future evolution of streamflow and annual maximum streamflow. MESH was forced by meteorological data from ERA5 reanalyses in the historical period (1979–2018) and 12 models of the Coupled model intercomparison Project Phase 5 (CMIP5) downscaled with the Canadian Regional Climate model version 5 (CRCM5) in historical (1979–2005) and scenario period (2006–2098). The projections consistently indicate an earlier spring flow and a reduction in the amount of annual maximum streamflow by the end of the 21st century. Under the RCP8.5 scenario, the annual maximum streamflow occurring in the spring is expected to be advanced by 2 weeks and reduced on average from 852 m3/s (±265) in the historical period (1979–2018) to 717m3/s (±250) by the end of the 21st century (2059–2098). Because the seasonal projection of streamflow was not investigated in previous studies, this work is an important first step to assess the seasonal change of streamflow in the Hudson Bay region under climate change.

Abstract Landslides involving sensitive clays are recurrent events in the world's northern regions and are especially notorious in eastern Canada. The two critical factors that separate sensitive clay landslides from traditional slope stability analysis are the highly brittle behavior in undrained conditions (strain-softening) characteristic of progressive or retrogressive failures and the large deformations associated with them. Conventional limit equilibrium analysis has numerous shortcomings in incorporating these characteristics when assessing landslides in sensitive clays. This paper presents an extensive literature review of the failure mechanics characteristics of landslides in sensitive clays and the existing constitutive models and numerical tools to analyze such slopes' stability and post-failure behavior. The advantages and shortcomings of the different techniques to incorporate strain-softening and large deformation in the numerical modeling of sensitive clay landslides are assessed. The literature review depicts that elastoviscoplastic soil models with non-linear strain-softening laws and rate effects represent the material behavior of sensitive clays. Though several numerical models have been proposed to analyze post-failure runouts, the amount of work performed in line with sensitive clay landslides is very scarce. That creates an urgent need to apply and further develop advanced numerical tools for better understanding and predicting these catastrophic events.

Abstract. Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine learning) methods to harness and integrate a broad variety of predictions from dynamical, physics-based models – such as numerical weather prediction, climate, land, hydrology, and Earth system models – into a final prediction product. They are recognized as a promising way of enhancing the prediction skill of meteorological and hydroclimatic variables and events, including rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. Hybrid forecasting methods are now receiving growing attention due to advances in weather and climate prediction systems at subseasonal to decadal scales, a better appreciation of the strengths of AI, and expanding access to computational resources and methods. Such systems are attractive because they may avoid the need to run a computationally expensive offline land model, can minimize the effect of biases that exist within dynamical outputs, benefit from the strengths of machine learning, and can learn from large datasets, while combining different sources of predictability with varying time horizons. Here we review recent developments in hybrid hydroclimatic forecasting and outline key challenges and opportunities for further research. These include obtaining physically explainable results, assimilating human influences from novel data sources, integrating new ensemble techniques to improve predictive skill, creating seamless prediction schemes that merge short to long lead times, incorporating initial land surface and ocean/ice conditions, acknowledging spatial variability in landscape and atmospheric forcing, and increasing the operational uptake of hybrid prediction schemes.

Abstract. The amount and phase of cold season precipitation accumulating in the upper Saint John River basin are critical factors in determining spring runoff, ice-jams, and flooding in downstream communities. To study the impact of winter and spring storms on the snowpack in the upper Saint John River (SJR) basin, the Saint John River Experiment on Cold Season Storms (SAJESS) utilized meteorological instrumentation, upper air soundings, human observations, and hydrometeor macrophotography during winter/spring 2020–21. Here, we provide an overview of the SAJESS study area, field campaign, and existing data networks surrounding the upper SJR basin. Initially, meteorological instrumentation was co-located with an Environment and Climate Change Canada station near Edmundston, New Brunswick, in early December 2020. This was followed by an intensive observation period that involved manual observations, upper-air soundings, a multi-angle snowflake camera, macrophotography of solid hydrometeors, and advanced automated instrumentation throughout March and April 2021. The resulting datasets include optical disdrometer size and velocity distributions of hydrometeors, micro rain radar output, near-surface meteorological observations, and wind speed, temperature, pressure and precipitation amounts from a K63 Hotplate precipitation gauge, the first one operating in Canada. These data are publicly available from the Federated Research Data Repository at https://doi.org/10.20383/103.0591 (Thompson et al., 2022). We also include a synopsis of the data management plan and data processing, and a brief assessment of the rewards and challenges of utilizing community volunteers for hydro-meteorological citizen science.

The magnitudes of dissolved organic carbon (DOC) exports from boreal peatlands to streams through lateral subsurface flow vary during the ice-free season. Peatland water table depth and the alternation of low and high flow in peat-draining streams are thought to drive this DOC export variability. However, calculation of the specific DOC exports from a peatland can be challenging considering the multiple potential DOC sources within the catchment. A calculation approach based on the hydrological connectivity between the peat and the stream could help to solve this issue, which is the approach used in the present research. This study took place from June 2018 to October 2019 in a boreal catchment in northeastern Canada, with 76.7 % of the catchment being covered by ombrotrophic peatland. The objectives were to (1) establish relationships between DOC exports from a headwater stream and the peatland hydrology; (2) quantify, at the catchment scale, the amount of DOC laterally exported to the draining stream; and (3) define the patterns of DOC mobilization during high-river-flow events. At the peatland headwater stream outlet, the DOC concentrations were monitored at a high frequency (hourly) using a fluorescent dissolved organic matter (fDOM) sensor, a proxy for DOC concentration. Hydrological variables, such as stream outlet discharge and peatland water table depth (WTD), were continuously monitored at hourly intervals for 2 years. Our results highlight the direct and delayed control of subsurface flow from peat to the stream and associated DOC exports. Rain events raised the peatland WTD, which increased hydrological connectivity between the peatland and the stream. This led to increased stream discharge (Q) and a delayed DOC concentration increase, typical of lateral subsurface flow. The magnitude of the WTD increase played a crucial role in influencing the quantity of DOC exported. Based on the observations that the peatland is the most important contributor to DOC exports at the catchment scale and that other DOC sources were negligible during high-flow periods, we propose a new approach to estimate the specific DOC exports attributable to the peatland by distinguishing between the surfaces used for calculation during high-flow and low-flow periods. In 2018–2019, 92.6 % of DOC was exported during flood events despite the fact that these flood events accounted for 59.1 % of the period. In 2019–2020, 93.8 % of DOC was exported during flood events, which represented 44.1 % of the period. Our analysis of individual flood events revealed three types of events and DOC mobilization patterns. The first type is characterized by high rainfall, leading to an important WTD increase that favours the connection between the peatland and the stream and leading to high DOC exports. The second is characterized by a large WTD increase succeeding a previous event that had depleted DOC available to be transferred to the stream, leading to low DOC exports. The third type corresponds to low rainfall events with an insufficient WTD increase to reconnect the peatland and the stream, leading to low DOC exports. Our results suggest that DOC exports are sensitive to hydroclimatic conditions; moreover, flood events, changes in rainfall regime, ice-free season duration, and porewater temperature may affect the exported DOC and, consequently, partially offset the net carbon sequestration potential of peatlands.

As Earth's atmospheric temperatures and human populations increase, more people are becoming vulnerable to natural and human-induced disasters. This is particularly true in Central America, where the growing human population is experiencing climate extremes (droughts and floods), and the region is susceptible to geological hazards, such as earthquakes and volcanic eruptions, and environmental deterioration in many forms (soil erosion, lake eutrophication, heavy metal contamination, etc.). Instrumental and historical data from the region are insufficient to understand and document past hazards, a necessary first step for mitigating future risks. Long, continuous, well-resolved geological records can, however, provide a window into past climate and environmental changes that can be used to better predict future conditions in the region. The Lake Izabal Basin (LIB), in eastern Guatemala, contains the longest known continental records of tectonics, climate, and environmental change in the northern Neotropics. The basin is a pull-apart depression that developed along the North American and Caribbean plate boundary ∼ 12 Myr ago and contains > 4 km of sediment. The sedimentological archive in the LIB records the interplay among several Earth System processes. Consequently, exploration of sediments in the basin can provide key information concerning: (1) tectonic deformation and earthquake history along the plate boundary; (2) the timing and causes of volcanism from the Central American Volcanic Arc; and (3) hydroclimatic, ecologic, and geomicrobiological responses to different climate and environmental states. To evaluate the LIB as a potential site for scientific drilling, 65 scientists from 13 countries and 33 institutions met in Antigua, Guatemala, in August 2022 under the auspices of the International Continental Scientific Drilling Program (ICDP) and the US National Science Foundation (NSF). Several working groups developed scientific questions and overarching hypotheses that could be addressed by drilling the LIB and identified optimal coring sites and instrumentation needed to achieve the project goals. The group also discussed logistical challenges and outreach opportunities. The project is not only an outstanding opportunity to improve our scientific understanding of seismotectonic, volcanic, paleoclimatic, paleoecologic, and paleobiologic processes that operate in the tropics of Central America, but it is also an opportunity to improve understanding of multiple geological hazards and communicate that knowledge to help increase the resilience of at-risk Central American communities.

Airborne LiDAR scanning is a promising approach to providing high-resolution products that are appropriate for different applications, such as flood management. However, the vertical accuracy of airborne LiDAR point clouds is not constant and varies in space. Having a better knowledge of their accuracy will assist decision makers in more accurately estimating the damage caused by flood. Data producers often report the total estimation of errors by means of comparison with a ground truth. However, the reliability of such an approach depends on various factors including the sample size, accessibility to ground truth, distribution, and a large enough diversity of ground truth, which comes at a cost and is somewhat unfeasible in the larger scale. Therefore, the main objective of this article is to propose a method that could provide a local estimation of error without any third-party datasets. In this regard, we take advantage of geostatistical ordinary kriging as an alternative accuracy estimator. The challenge of considering constant variation across the space leads us to propose a non-stationary ordinary kriging model that results in the local estimation of elevation accuracy. The proposed method is compared with global ordinary kriging and a ground truth, and the results indicate that our method provides more reliable error values. These errors are lower in urban and semi-urban areas, especially in farmland and residential areas, but larger in forests, due to the lower density of points and the larger terrain variations.